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Updated: Dec 2, 2025

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
Published on: June 16, 2017
International Consensus Definition of DNA Methylation Subgroups in Juvenile Myelomonocytic Leukemia
Maximilian Schönung1,2, Julia Meyer3, Peter Nöllke4
1Section Translational Cancer Epigenomics, Division Translational Medical Oncology, German Cancer Research Center (DKFZ) & National Center for Tumor Diseases (NCT), Heidelberg, Germany.
Purpose:
Known clinical and genetic markers have limitations in predicting disease course and outcome in juvenile myelomonocytic leukemia (JMML). DNA methylation patterns in JMML have correlated with outcome across multiple studies, suggesting it as a biomarker to improve patient stratification. However, standardized approaches to classify JMML on the basis of DNA methylation patterns are lacking. We, therefore, sought to define an international consensus for DNA methylation subgroups in JMML and develop classification methods for clinical implementation.
Experimental Design:
Published DNA methylation data from 255 patients with JMML were used to develop and internally validate a classifier model. Accuracy across platforms (EPIC-arrays and MethylSeq) was tested using a technical validation cohort (32 patients). The suitability of both methods for single-patient classification was demonstrated using an independent cohort (47 patients).
Results:
Analysis of pooled, published data established three DNA methylation subgroups as a de facto standard. Unfavorable prognostic parameters (PTPN11 mutation, elevated fetal hemoglobin, and older age) were significantly enriched in the high methylation (HM) subgroup. A classifier was then developed that predicted subgroups with 98% accuracy across different technological platforms. Applying the classifier to an independent validation cohort confirmed an association of HM with secondary mutations, high relapse incidence, and inferior overall survival (OS), while the low methylation subgroup was associated with a favorable disease course. Multivariable analysis established DNA methylation subgroups as the only significant factor predicting OS.
Conclusions:
This study provides an international consensus definition for DNA methylation subgroups in JMML. We developed and validated methods which will facilitate the design of risk-stratified clinical trials in JMML.
Insights
This study defines DNA methylation subgroups for juvenile myelomonocytic leukemia (JMML), creating a classifier to predict patient outcomes and guide risk-stratified clinical trials for better treatment strategies.
Area of Science:
- Pediatric Hematology Oncology
- Epigenetics
- Biomarker Discovery
Background:
- Clinical and genetic markers for juvenile myelomonocytic leukemia (JMML) have limited predictive power for disease course.
- DNA methylation patterns show promise as biomarkers for JMML patient stratification.
- Standardized methods for classifying JMML based on DNA methylation are currently lacking.
Purpose of the Study:
- To establish an international consensus for DNA methylation subgroups in JMML.
- To develop and validate classification methods for clinical implementation of DNA methylation profiling in JMML.
- To improve patient stratification and risk assessment in JMML.
Main Methods:
- Utilized published DNA methylation data from 255 JMML patients to develop a classifier model.
- Validated classifier accuracy across different platforms (EPIC-arrays, MethylSeq) using technical and independent patient cohorts.
- Assessed the suitability of developed methods for single-patient classification.
Main Results:
- Identified three DNA methylation subgroups in JMML, with a high methylation (HM) subgroup enriched for unfavorable prognostic factors.
- Developed a classifier with 98% accuracy across platforms, linking HM to secondary mutations, high relapse rates, and inferior overall survival (OS).
- Established DNA methylation subgroups as the sole significant predictor of OS in multivariable analysis.
Conclusions:
- Provided an international consensus definition for JMML DNA methylation subgroups.
- Developed and validated robust classification methods for clinical use.
- Facilitated the design of future risk-stratified clinical trials for JMML.
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